Project - Bank Churn Prediction

Background:

Businesses like banks that provide service have to worry about the problem of 'Churn' i.e. customers leaving and joining another service provider. It is important to understand which aspects of the service influence a customer's decision in this regard. Management can concentrate efforts on the improvement of service, keeping in mind these priorities.

Objective:

To identify different segments in the existing customer based on their spending patterns as well as past interaction with the bank.

Dataset:

https://www.kaggle.com/barelydedicated/bank-customer-churn-modeling

Attributes:

Import Necessary Libraries & Format Notebook

Exploratory Data Analysis

Initial Data Observations

Observations:

Data Preparation:

Split the dataset into train, validation, and test sets

Create Helper Functon(s)

Build Models

ReLU - SGD - 1 Hidden Layer

Sigmoid - SGD - 1 Hidden Layer

Tanh - SGD - 1HL

Model Improvements

ReLU - Adam - 1 Hidden Layer

ReLU - RMSProp - 1 Hidden Layer

ReLU - RMSProp - 2 Hidden Layers

ReLU - RMSprop - 1 Hidden Layer - SMOTE

Conclusion

Remarks: